Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
arXiv:2604.00536v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive domains such as humanities, social sciences, medicine, law, and finance is scarce because expert curation is expensive, privacy constraints are strict, and label consistency is hard to ensure. Recent work uses synthetic data, typically by prompting a generator over domain documents and filtering outputs with handcrafted rubrics. Yet rubric design is expert-dependent, transfers poorly across domains, and is often optimized through a brittle heuristic loop of writing rubrics, synthesizing data, training, inspecting results, and manually guessing revisions. This process lacks reliable quantitative feedback about how a rubric affects downstream performance. We propose evaluating synthetic data by its training utility on the target model and using this signal to gui
arXiv:2604.00536v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive domains such as humanities, social sciences, medicine, law, and finance is scarce because expert curation is expensive, privacy constraints are strict, and label consistency is hard to ensure. Recent work uses synthetic data, typically by prompting a generator over domain documents and filtering outputs with handcrafted rubrics. Yet rubric design is expert-dependent, transfers poorly across domains, and is often optimized through a brittle heuristic loop of writing rubrics, synthesizing data, training, inspecting results, and manually guessing revisions. This process lacks reliable quantitative feedback about how a rubric affects downstream performance. We propose evaluating synthetic data by its training utility on the target model and using this signal to guide data generation. Inspired by influence estimation, we adopt an optimizer-aware estimator that uses gradient information to quantify each synthetic sample's contribution to a target model's objective on specific tasks. Our analysis shows that even when synthetic and real samples are close in embedding space, their influence on learning can differ substantially. Based on this insight, we propose an optimization-based framework that adapts rubrics using target-model feedback. We provide lightweight guiding text and use a rubric-specialized model to generate task-conditioned rubrics. Influence score is used as the reward to optimize the rubric generator with reinforcement learning. Experiments across domains, target models, and data generators show consistent improvements and strong generalization without task-specific tuning.
Executive Summary
Optimsyn, a novel framework for synthetic data generation, addresses the limitations of traditional rubric design by leveraging influence estimation and reinforcement learning. By quantifying the contribution of each synthetic sample to a target model's objective, Optimsyn optimizes rubrics for improved downstream performance. This approach offers consistent improvements and strong generalization across domains, target models, and data generators, making it a promising solution for knowledge-intensive domains where high-quality supervised fine-tuning data is scarce. The framework's adaptability and flexibility are key advantages, enabling researchers to tailor rubrics to specific tasks and models without extensive domain expertise. Optimsyn's potential to accelerate research in various fields, including humanities, social sciences, medicine, law, and finance, is substantial, and its impact on the generation of high-quality synthetic data is likely to be significant.
Key Points
- ▸ Optimsyn uses influence estimation to quantify the contribution of each synthetic sample to a target model's objective.
- ▸ The framework optimizes rubrics for improved downstream performance using reinforcement learning.
- ▸ Optimsyn offers consistent improvements and strong generalization across domains, target models, and data generators.
Merits
Strength
Adaptability and flexibility in rubric design enable researchers to tailor the framework to specific tasks and models without extensive domain expertise.
Improved Downstream Performance
Optimsyn's optimization-based framework adapts rubrics for improved downstream performance, leading to consistent improvements and strong generalization.
Scalability
The framework's ability to handle large datasets and complex models makes it a promising solution for knowledge-intensive domains where high-quality supervised fine-tuning data is scarce.
Demerits
Limitation
The reliance on reinforcement learning may introduce additional computational costs and require substantial expertise in machine learning and reinforcement learning techniques.
Data Quality
The quality of synthetic data generated by Optimsyn may still be dependent on the quality of the input data and the rubric design, which can be challenging to optimize.
Transferability
The framework's performance may not generalize well to new domains or tasks, requiring additional fine-tuning and adaptation efforts.
Expert Commentary
Optimsyn represents a significant advancement in synthetic data generation, addressing the limitations of traditional rubric design. The framework's adaptability and flexibility are key advantages, enabling researchers to tailor rubrics to specific tasks and models without extensive domain expertise. However, the reliance on reinforcement learning may introduce additional computational costs and require substantial expertise in machine learning and reinforcement learning techniques. Furthermore, the quality of synthetic data generated by Optimsyn may still be dependent on the quality of the input data and the rubric design. Nonetheless, the framework's potential to accelerate research in various fields is substantial, and its impact on the generation of high-quality synthetic data is likely to be significant.
Recommendations
- ✓ Further research is needed to explore the limitations and potential biases of Optimsyn, particularly in domains where data quality is critical.
- ✓ The framework's performance should be evaluated on a wide range of tasks and models to ensure its generalizability and adaptability.
- ✓ Developing tools and interfaces that facilitate the use of Optimsyn by researchers without extensive machine learning expertise is essential for widespread adoption.
Sources
Original: arXiv - cs.CL
